Automatic acquisition of script knowledge from a text collection

Fujiki, Takashi; Nanba, Hidetsugu; Okumura, Manabu

doi:10.3115/1067737.1067756

Cited by 10 publications

(8 citation statements)

References 2 publications

(1 reference statement)

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“…For example, when a customer goes to a restaurant, he or she usually 'enters the restaurant', 'waits', 'sits down', 'gets the menu and decides what to eat', 'orders the dish', 'waits until the dish has come', and so on. This sequence can be said to be script knowledge in the situation of 'eating at a restaurant' [12]. Now, if someone wants to represent this stereotype knowledge then one has to further specify script name, track, entry conditions, props, roles, scenes and results.…”

Section: Scriptsmentioning

confidence: 99%

Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes

Ali

Khan

2009

2009 International Conference on Emerging Technologies

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In Artificial Intelligence, knowledge representation is a combination of data structures and interpretive procedures that leads to knowledgeable behavior. Therefore, it is required to investigate such knowledge representation technique in which knowledge can be easily and efficiently represented in computer. This research paper compares various knowledge representation techniques and proves that predicate logic is a more efficient and more accurate knowledge representation scheme. The algorithm in this paper splits the English text/sentences into phrases/constituents and then represents these in predicate logic. This algorithm also generates the original sentences from the representation in order to check the accuracy of representation. The algorithm has been tested on real text/sentences of English. The algorithm has achieved an accuracy of 80%. If the text is in simple discourse units, then the algorithm accurately represents it in predicate logic. The algorithm also accurately retrieves the original text/sentences from such representation.

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Section: Scriptsmentioning

confidence: 99%

Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes

Ali

Khan

2009

2009 International Conference on Emerging Technologies

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“…While human reliance on sexual scripts suffers from the weakness explained by Gangi, the situation will be very different for future generations of sexbots, partly because a human's repertoire of sexual scripts will inevitably be rather sparse, whereas robots in decades to come will have access to huge collections of scripts on just about every subject under the sun, including every brand of seduction, sexual behaviour and lovemaking technique. We can therefore expect sexbots to be able to call upon a "big data" collection of sexual scripts.³ The automatic acquisition of script knowledge from a text collection [14] will enable the creation of big data sets by aggregating all the knowledge found in books and other media on the subject of sexual relationships and love- 2 I have deliberately transposed the order of the second and third paragraphs from Gangi's original [8].…”

Section: How Robot Intelligence Will Employ Sexual Scriptsmentioning

confidence: 99%

Some aspects of human consent to sex with robots

Levy¹

2020

Paladyn, Journal of Behavioral Robotics

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AbstractPart of the ethical debate about sex with robots concerns whether sex with a robot is rape of that robot. It therefore makes sense for us to debate what should be the boundaries of consent, decades from now, i.e. consent given by humans to robots. How will the sexbot landscape look in situations when it is the human who is consenting, or not, to a sexual invitation or advance by the robot? The sexbot will have responsibilities towards its human partner, and there will be moral and legal consequences if it fails to deliver on those responsibilities. An unresolved ethical argument employed by many of those who deplore the coming advent of sex robots is that robots are unable to proffer a meaningful indication of sexual consent, and therefore a human deciding to have sex with a robot is committing rape of the robot. A parallel question, as yet to be addressed, is under what circumstances should a robot be considered to be acting in a sexually inappropriate or illegal manner towards a human? And this question embraces some others, including: “How can a robot determine, with any degree of certainty, whether or not a proximate human wants or at least consents to sex?”; “What behaviours by a robot are permissible within the #MeToo context when the robot is exploring a proximate human’s current level of sexual interest in the robot?”; and “If a robot oversteps the accepted bounds of sexual behaviour with a human, who is responsible and what should be the legal consequences?” We discuss these issues and speculate on how the sex robots of the future will be able to conform to the ethics of consent.

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“…Certain features are desirable for the perception delineation [27] in terms of apparent, emphatic, better expressiveness, dexterous, influential, and independent of the circumstances [28]. The tree much robust knowledge base [29] that can accumulate the regain intimation from the collection of research paper.…”

Section: B the Knowledge Tree Formationmentioning

confidence: 99%

An Efficient Mining for Recommendation System for Academics

Akhtar¹,

Agarwal²

2020

IJRTE

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At present time huge numbers of research articles are available on World Wide Web in any domain. The research scholar explores a research papers to get the appropriate information and it takes time and effort of the researcher. In this scenario, there is the need for a researcher to search a research based on its research article. In the present paper a method of Knowledge ablation from a collection of research articles, is presented to evolve a system research paper recommendation system (RPRS), which would generate the recommendations for research article based on researcher choice. The RPRS accumulate the knowledge ablated from the pertinent research articles in the form of semantic tree. It accumulates all the literal sub parts with their reckoning in nodes. These parts are arranged based on their types in such a way that the leaf nodes stores the words with its prospect, the higher layer gives details about dictum with its reckoning, next to it an abstract. A Bayesian network is applied to construct a verisimilitude model which would quotation the pertinent tidings from the knowledge tree to construct the recommendation and word would be scored through TF-IDF value

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Automatic acquisition of script knowledge from a text collection

Cited by 10 publications

References 2 publications

Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes

Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes

Some aspects of human consent to sex with robots

An Efficient Mining for Recommendation System for Academics

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